Model components / Benchmark / eval datasets

Scale SEAL Leaderboard

Scale AI maintains proprietary evaluation datasets used for its SEAL (Systematic Evaluation of AI Language models) Leaderboard, including coding-specific eval sets. Problems are held private to prevent contamination and scored by expert annotators. Used by enterprise customers to evaluate models before deployment. The commercial alternative to public benchmarks, trading reproducibility for contamination resistance.

Scale AI SEAL Leaderboards (Safety, Evaluations, and Alignment Lab); launched 2024, regularly updated with new domains (instruction-following, math, coding, multilinguality, etc.). The scored artifact is the private held-out eval sets behind the leaderboard. Verified live June 2026 via labs.scale.com/leaderboard (redirect from scale.com/leaderboard) and scale.com/blog/leaderboard.

Openness

1 high confidence

1.0

data: closed(proprietary held-out prompt sets, ~1,000 examples/domain, deliberately unpublished to prevent contamination/training-set leakage)
datasheet: no(methodology described but examples not redistributable)
license: none(not distributed)
access: held-out(results visible on leaderboard, dataset not downloadable)

Intentionally private/held-out eval sets per the data-openness rubric (closed = proprietary held-out test suite); only the leaderboard results are public, the data itself is not.

https://scale.com/blog/leaderboard recorded 2026-06-04
Scale states SEAL datasets remain private/unpublished, combining proprietary held-out sets (~1,000 examples) with some open source sets

Adoption

3 medium confidence

3.0

SEAL is a widely-referenced expert-driven private leaderboard cited across the LLM evaluation community as a contamination-resistant ranking; broad press and industry attention but no published download/usage count (the data is held-out by design). Reported-traction tier as an industry-standard reference rather than a measured usage_volume figure.

https://scale.com/blog/leaderboard recorded 2026-06-04
SEAL positioned as a trusted, tamper-proof frontier-model leaderboard used to rank major LLMs

Capability

not assessed

Capability is not a meaningful axis for an eval dataset; openness + adoption carry this category per the recipe.

Unchanged since 2026-06-24 (last edited, not re-checked)